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DunnBC22
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trocr-base-printed_license_plates_ocr

Image-to-Text
Transformers
PyTorch
English
vision-encoder-decoder
image-text-to-text
Generated from Trainer
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trocr-base-printed_license_plates_ocr / README.md
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metadata
tags:
  - generated_from_trainer
model-index:
  - name: trocr-base-printed_license_plates_ocr
    results: []
language:
  - en
metrics:
  - cer
pipeline_tag: image-to-text

trocr-base-printed_license_plates_ocr

This model is a fine-tuned version of microsoft/trocr-base-printed on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1581
  • Cer: 0.0368

Model description

This model extracts text from image input (License Plates).

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Optical%20Character%20Recognition%20(OCR)/OCR%20License%20Plates/OCR_license_plate_text_recognition.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/nickyazdani/license-plate-text-recognition-dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Cer
0.3144 1.0 2000 0.2463 0.0473
0.143 2.0 4000 0.1581 0.0368

Framework versions

  • Transformers 4.21.3
  • Pytorch 1.12.1
  • Datasets 2.4.0
  • Tokenizers 0.12.1